Essential Computing for Bioinformatics First Steps in Computing: Course Overview

(1)

MARC: Developing Bioinformatics Programs

July 2009

Alex Ropelewski

PSC-NRBSC

Bienvenido Vélez

UPR Mayaguez

Reference: How to Think Like a Computer Scientist: Learning with Python

Essential Computing for Bioinformatics

First Steps in Computing: Course Overview

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• The following material is the result of a curriculum development effort to provide a set

of courses to support bioinformatics efforts involving students from the biological sciences, computer science, and mathematics departments. They have been developed as a part of the NIH funded project “Assisting Bioinformatics Efforts at Minority Schools” (2T36 GM008789). The people involved with the curriculum development effort include:

• Dr. Hugh B. Nicholas, Dr. Troy Wymore, Mr. Alexander Ropelewski and Dr. David

Deerfield II, National Resource for Biomedical Supercomputing, Pittsburgh Supercomputing Center, Carnegie Mellon University.

• Dr. Ricardo González Méndez, University of Puerto Rico Medical Sciences Campus.

• Dr. Alade Tokuta, North Carolina Central University.

• Dr. Jaime Seguel and Dr. Bienvenido Vélez, University of Puerto Rico at Mayagüez.

• Dr. Satish Bhalla, Johnson C. Smith University.

• Unless otherwise specified, all the information contained within is Copyrighted © by

Carnegie Mellon University. Permission is granted for use, modify, and reproduce these materials for teaching purposes.

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

Course Overview



Introduction to Programming (Today)



Why learn to Program?



The Python Interpreter



Software Development Process



Numbers, Strings, Operators, Expressions



Control structures, decisions, iteration and

recursion

Outline

(4)

Course Overview

•

–

Course Description

–

Educational Objectives

–

Major Course Modules

–

Module Descriptions

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Course Description

This course provides a broad introductory discussion

of essential computer science concepts that have wide

applicability in the natural sciences. Particular

emphasis will be placed on applications to

Bioinformatics. The concepts will be motivated by

practical problems arising from the use of

bioinformatics research tools such as genetic

sequence databases. Concepts will be discussed in a

weekly lecture and will be practiced via simple

programming exercises using Python, an easy to learn

and widely available scripting language.

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Educational Objectives

•

Awareness of the mathematical models of computation and their

fundamental limits

•

Basic understanding of the inner workings of a computer system

•

Ability to extract useful information from various bioinformatics data

sources

•

Ability to design computer programs in a modern high level language

to analyze bioinformatics data.

•

Experience with commonly used software development environments

and operating systems

•

Experience applying computer programming to solve bioinformatics

problems

(7)

Major Course Modules

Module

Lecture

MARC

Lecture

First Steps in Computing: Course Overview 1

Using Bioinformatics Data Sources 2

Mathematical Computing Models 3 5

High-level Programming (Python): Flow Control 6 2

High-level Programming (Python): Container Objects 7 3

High-level Programming (Python): Files 8 4

High-level Programming (Python): BioPython 9

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Main Advantages of Python

•

Familiar to C/C++/C#/Java Programmers

•

Very High Level

•

Interpreted and Multi-platform

•

Dynamic

•

Object-Oriented

•

Modular

•

Strong string manipulation

•

Lots of libraries available

•

Runs everywhere

•

Free and Open Source

•

Track record in bioInformatics (BioPython)

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Using BioInformatics Data Sources

•

Searching Nucleotide Sequence Databases

•

Searching Amino Acid Sequence Database

•

Performing BLAST Searches

•

Using Specialized Data Sources

IDEA: How can we expedite data collection and analysis?

... writing programs to automate parts of the process.

9 These materials were developed with funding from the US National Institutes of Health grant #2T36 GM008789 to the Pittsburgh Supercomputing Center

Reference:

Bioinformatics for Dummies (Ch 1-4)

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Mathematical Computing

Goal:General Awareness

•

What is Computing?

•

Mathematical Models of Computing

–

Finite Automata

–

Turing Machines

•

The Limits of Computation

•

Church/Turing Thesis

•

What is an Algorithm?

•

Big O Notation

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High-Level Programming (Python)

•

Downloading and Installing the Interpreter

•

Values, Expressions and Naming

•

Designing your own Functional Building

Blocks

•

Controlling the Flow of your Program

•

String Manipulation (Sequence Processing)

•

Container Data Structures

•

File Manipulation

11 These materials were developed with funding from the US National Institutes of Health grant #2T36 GM008789 to the Pittsburgh Supercomputing Center

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CS Fundamentals will be Interleaved Throughout the Course

•

Information Representation and Encoding

•

Computer Architecture

•

Programming Language Translation Methods

•

The Software Development Cycle

•

Fundamental Principles of Software

Engineering

•

Basic Data Structures for Bioinformatics

•

Design and Analysis of Bioinformatics

Algorithms

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US Department of Labor, Bureau of Labor Statistics

Engineers, Life and Physical Scientists and Related Occupations.

Occupational Outlook Handbook, 2008-09 Edition

.

Biological scientists “…usually study allied disciplines

such as mathematics, physics, engineering and

computer science.

Computer courses are beneficial for

modeling and simulating biological processes, operating

some laboratory equipment and performing research in

the emerging field of bioinformatics

”

Why Learn to Program?

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Why Learn to Program?

14

 

Need to compare output from a new run with an old run. (new hits in

database search)

 

Need to compare results of runs using different parameters. (Pam120

vs Blosum62)

 

Need to compare results of different programs (Fasta, Blast,

Smith-Waterman)

 

Need to modify existing scripts to work with new/updated programs and

web sites.

 

Need to use an existing program's output as input to a different

program, not designed for that program:



Database search -> Multiple Alignment



Multiple Alignment -> Pattern search

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Bioinformatics Assembly Analyst

Responsibilities:

 Assembling genome sequence data using a variety of tools and parameters and performing the

experiments needed to evaluate sequencing strategies

 Using existing software and databases to analyze genomic data and correlating assemblies and

sequences with a variety of genetic and physical maps and other biological information

 Identifying problems and serving as point of contact for various groups to propose and

implement solutions

 Proposing and implementing upgrades to existing tools and processes to enhance analysis

techniques and quality of results

 Developing and implementing scripts to manipulate, format, parse, analyze, and display

genome sequence data; and developing new strategies for analysis and presentation of results.

Requirements:

 A bachelor's degree in biology or related field

 At least three years of experience in DNA sequencing and sequence analysis.

 Must possess solid knowledge of sequencing software and public sequencing databases.

 Knowledge of bioinformatics tools helpful.

Why Learn to Program?

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

C/C++



Language of choice for most large development projects



FORTRAN



Excellent language for math, not used much anymore



Java



Popular modern object oriented language



PERL



Excellent language for text-processing (bioperl.org)



PHP



Popular language used to program web interfaces



Python



Language easy to pick up and learn (biopython.org)



SQL



Language used to communicate with a relational database

Good Languages to Learn

In no particular order….

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

“Object Oriented” is simply a convenient way to organize your

data and the functions that operate on that data



A biological example of organizing data:

 

Human.CytochromeC.protein.sequence

 

Human.CytochromeC.RNA.sequence

 

Human.CytochromeC.DNA.sequence



Some things only make sense in the context that they are used:

 

Human,CytochromeC.DNA.intron

 

Human.CytochromeC.DNA.exon

 

Human.CytochromeC.DNA.sequence

 

Human.CytochromeC.protein.sequence

 

Human.CytochromeC.protein.intron

 

Human.CytochromeC.protein.exon

Python is Object Oriented

17

Meaningful

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

Go to

www.python.org



Go to DOWNLOAD section



Click on

Python 2.6.2 Windows installer



Save ~10MB file into your hard drive



Double click on file to install



Follow instructions



Start -> All Programs -> Python 2.6 -> Idle

Downloading and Installing Python

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Idle: The Python Shell

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Python as a Number Cruncher

20

>>> print 1 + 3

4

>>> print 6 * 7

42

>>> print 6 * 7 + 2

44

>>> print 2 + 6 * 7

44

>>> print 6 - 2 - 3

1

>>> print 6 - ( 2 - 3)

7

>>> print 1 / 3

0

>>>

/ and * higher precedence than + and -

integer division truncates fractional part Operators are left associative

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Floating Point Expressions

21

>>> print 1.0 / 3.0

0.333333333333

>>> print 1.0 + 2

3.0

>>> print 3.3 * 4.23

13.959

>>> print 3.3e23 * 2

6.6e+023

>>> print float(1) /3

0.333333333333

>>>

Mixed operations converted to float

Scientific notation allowed

12 decimal digits default precision

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String Expressions

22 >>> print "aaa" aaa >>> print "aaa" + "ccc" aaaccc >>> len("aaa") 3 >>> len ("aaa" + "ccc") 6 >>> print "aaa" * 4 aaaaaaaaaaaa >>> "aaa" 'aaa' >>> "c" in "atc" True >>> "g" in "atc" False >>> + concatenates string

len is a function that returns the length of its argument string

any expression can be an argument

* replicates strings

a value is an expression that yields itself

in operator finds a string inside another And returns a boolean result

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23

>>> numAminoAcids = 20 >>> eValue = 6.022e23

>>> prompt = "Enter a sequence ->" >>> print numAminoAcids 20 >>> print eValue 6.022e+023 >>> print prompt Enter a sequence -> >>> print "prompt" prompt >>> >>> prompt = 5 >>> print prompt 5 >>>

= binds a name to a value

prints the value bound to a name

= can change the value associated with a name even to a different type

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Values Have Types

24

>>> type("hello")

>>> type(3)

>>> type(3.0)

>>> type(eValue)

>>> type (prompt)

>>> type(numAminoAcids)

>>>

type is another function

the type of a name is the type of the

value bound to it

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In Bioinformatics Words …

25 >>> codon=“atg” >>> codon * 3 ’atgatgatg’ >>> seq1 =“agcgccttgaattcggcaccaggcaaatctcaaggagaagttccggggagaaggtgaaga” >>> seq2 = “cggggagtggggagttgagtcgcaagatgagcgagcggatgtccactatgagcgataata” >>> seq = seq1 + seq2

>>> seq 'agcgccttgaattcggcaccaggcaaatctcaaggagaagttccggggagaaggtgaagacggggagtggggagttgagtc gcaagatgagcgagcggatgtccactatgagcgataata‘ >>> seq[1] 'g' >>> seq[0] 'a' >>> “a” in seq True >>> len(seq1) 60 >>> len(seq) 120

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More Bioinformatics

Extracting Information from Sequences

26 >>> seq[0] + seq[1] + seq[2]

’agc’ >>> seq[0:3] ’agc’ >>> seq[3:6] ’gcc’ >>> seq.count(’a’) 35 >>> seq.count(’c’) 21 >>> seq.count(’g’) 44 >>> seq.count(’t’) 12 >>> long = len(seq) >>> pctA = seq.count(’a’) >>> float(pctA) / long * 100 29.166666666666668

Find the first codon from the sequence get ’slices’ from strings:

How many of each base does this sequence contain?

Count the percentage of each base on the sequence.

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Additional Note About Python Strings

27

>>> seq=“ACGT”

>>> print seq

ACGT

>>> seq=“TATATA”

>>> print seq

TATATA

>>> seq[0] = seq[1]

Traceback (most recent call last):

File "<pyshell#33>", line 1, in <module>

seq[0]=seq[1]

TypeError: 'str' object does not support item assignment

seq = seq[1] + seq[1:]

Can replace

one whole

string with

another

whole string

Can

NOT

simply replace

a sequence

character with

another

sequence

character, but…

Can replace a whole string using substrings

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

How?

 

Precede comment with # sign

 

Interpreter ignores rest of the line



Why?

 

Make code more readable by others AND yourself?



When?

 

When code by itself is not evident

# compute the percentage of the hour that has elapsed

percentage = (minute * 100) / 60

 

Need to say something but Python cannot express it, such as

documenting code changes

percentage = (minute * 100) / 60 # FIX: handle float division

Commenting Your Code!

28

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

Problem Identification

 

What is the problem that we are solving



Algorithm Development

 

How can we solve the problem in a step-by-step manner?



Coding

 

Place algorithm into a computer language



Testing/Debugging

 

Make sure the code works on data that you already know the

answer to



Run Program

 

Use program with data that you do not already know the answer to.

Software Development Cycle

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

First, lets learn to SAVE our programs in a file:



From Python Shell: File -> New Window



From New Window: File->Save



Then, To run the program in the new window:



From New Window: Run->Run Module

Lets Try It With Some Examples!

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

What is the percentage composition of a nucleic

acid sequence



DNA sequences have four residues, A, C, G,

and T



Percentage composition means the percentage

of the residues that make up of the sequence

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

Print the sequence



Count characters to determine how many A, C, G

and T’s make up the sequence



Divide the individual counts by the length of the

sequence and take this result and multiply it by

100 to get the percentage



Print the results

Algorithm Development

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Coding

33

seq="ACTGTCGTAT"

print seq

Acount= seq.count('A')

Ccount= seq.count('C')

Gcount= seq.count('G')

Tcount= seq.count('T')

Total = len(seq)

APct = int((Acount/Total) * 100)

print 'A percent = %d ' % APct

CPct = int((Ccount/Total) * 100)

print 'C percent = %d ' % CPct

GPct = int((Gcount/Total) * 100)

print 'G percent = %d ' % GPct

TPct = int((Tcount/Total) * 100)

print 'T percent = %d ' % TPct

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

First SAVE the program:



From New Window: File->Save

Let’s Test The Program

(35)



Six Common Python Coding Errors:

 

Delimiter mismatch: check for matches and proper use.

 

Single and double quotes: ‘ ’ “ ”

 

Parenthesis and brackets: { } [ ] ( )

 

Spelling errors:

 

Among keywords

 

Among variables

 

Among function names

 

Improper indentation

 

Import statement missing

 

Function calling parameters are mismatched

 

Math errors:

 

Automatic type conversion: Integer vs floating point

 

Incorrect order of operations – always use parenthesis.

Testing / Debugging

(36)

seq='ACTGTCGTAT"

print seq;

Tcount= seq,count('T')

Total = Len(seq)

APct = int((Acount/Total) * 100)

print 'C percent = %d ' % Cpct

GPct = int(Gcount/Total) * 100)

primt 'G percent = %d ' % GPct

Testing / Debugging

36

(37)



First, re-SAVE the program:



File->Save



Then RUN the program:



Run->Run Module



Then LOOK at the Python Shell Window:



If successful, the results are displayed



If unsuccessful, error messages will be

displayed

Let’s Test The Program

(38)



The program says that the composition is:



0%A, 0%C, 0%G, 0%T



The real answer should be:



20%A, 20%C, 20%G, 40%T



The problem is in the coding step:



Integer math is causing undesired rounding!

Testing/Debugging

(39)

seq="ACTGTCGTAT"

print seq

Tcount= seq.count('T')

Total =

float(

len(seq)

)

print 'G percent = %d ' % GPct

Testing/Debugging

39

(40)



If the first line was changed to:



seq = “ACUGCUGUAU”



Would we get the desired result?

Let’s change the nucleic acid sequence from

DNA to RNA…

(41)



The program says that the composition is:



20%A, 20%C, 20%G, 0%T



The real answer should be:



20%A, 20%C, 20%G, 40%U



The problem is that we have not defined the problem

correctly!



We designed our code assuming input would be

DNA sequences



We fed the program RNA sequences

Testing/Debugging

(42)



What is the percentage composition of a nucleic

acid sequence



DNA sequences have four residues, A, C, G,

and T



In RNA sequences “U” is used in place of “T”



Percentage composition means the percentage

of the residues that make up of the sequence

(43)



Print the sequence



Count characters to determine how many A, C, G,

T

and U’s

make up the sequence



Divide the individual

A,C,G

counts

and the sum of

T’s and U’s

by the length of the sequence and take

this result and multiply it by 100 to get the

percentage



Print the results

Algorithm Development

(44)

Testing/Debugging

44

seq="ACUGUCGUAU"

print seq

TUcount

= seq.count('T')

+ seq.count(‘U')

Total = float(len(seq))

print 'G percent = %d ' % GPct

TUPct

= int((

TUcount

/Total) * 100)

print 'T

/U

percent = %d ' %

TUPct

(45)



Extend your code to handle the nucleic acid

ambiguous sequence characters “N” and “X”



Extend your code to handle protein sequences

What’s Next